Final Program schedule

Keynotes

TITLE: Spontaneous brain activity: a key for understanding the mind and the pathophysiology of brain diseases.

Traditional accounts of brain function (Hubel and Wiesel 1968) emphasize the role of feedforward information processing in generating from the ’ground up’ sensory, cognitive, and motor representations that implement behavior. Such feedforward ’sensory-motor’ models have been successful in linking activity recorded from single neurons to perceptual decisions 1.

However, a different class of models suggests that the brain is not a passive analyzer driven by sensory information, but that it actively generates and maintains predictions (priors) about forthcoming sensory stimuli, cognitive states and actions 2. This class of models emphasizes the role of spontaneous activity in maintaining active representations that are modulated rather than determined by sensory information. Accordingly, spontaneous activity should not be random (as often implied by its dismissal as mere ’noise’), but organized into structured spatiotemporal profiles that reflect the functional architecture of the brain, possibly encode traces of previous behavior, or even predict future decisions.

Experimental and theoretical evidences reveal that the spontaneous ongoing activity of local cortical circuits result from a global balance between excitatory and inhibitory synaptic currents. At the level of large-scale cortical circuits, organized spatiotemporal patterns of spontaneous or intrinsic activity have been recently described in the resting state, hence Resting State Networks (RSNs) 3. In my lecture I will describe the topography and functional organization of RSNs in the human brain, their neurophysiological basis, and their potential functional significance 4, 5. Finally, the importance of RSNs for clinical neurology as a bioassay of brain function will be illustrated in the case of stroke, in whom we observe that abnormality of interhemispheric communication, even in the absence of structural damage, seems to correlate with behavioral deficits of movement and attention 6.

MEG allows the non-invasive localisation and characterisation of a range of cortical oscillatory phenomena. These are increasingly thought to reflect both local and network properties of the neural populations underpinning various perceptual and cognitive functions. Studies reveal complex, task-specific and spatially localised effects, some of which appear to be co-localised with the BOLD-fMRI response to the same task. In this talk I shall explain some of the methodology behind these studies and how these human MEG signals compare with invasive recordings in both animals and humans.

My main focus will be on how individual differences in oscillatory parameters, such as amplitude and frequency, can be related to variations in perceptual/behavioural task performance and to individual differences in neurotransmitter concentrations. For example, recent work by ourselves and others have demonstrated that visual gamma frequency appears to be a stable trait-marker in healthy controls and is correlated with individual variability in bulk measures of GABAergic inhibition. However, peak frequency is also sensitive to age-related effects and has recently been shown to correlate with structural parameters within the visual system, such as the area and thickness of V1.

I will also talk about the relevance of these studies to clinical conditions such as epilepsy and schizophrenia. If it is indeed true that individual variability in oscillatory dynamics is a sensitive biomarker of synaptic properties within the cortex, then these robust measures have much to offer in terms of endophenotyping and links to both behavioural and genetic markers of disease state, particularly as peak gamma frequency has recently been shown to be highly heritable.

Finally, given that low-level oscillatory measures appear sensitive to variability in cortical excitation/inhibition parameters, they offer new opportunities for studying the effects of pharmacological agents, both in terms of assessing drug effects on synaptic function, but also in helping to understand individual variability in treatment response and pharmacodynamics. Pharmaco-MEG has a key advantage over fMRI in this application, as it is not confounded by drug-related systemic changes in physiology or direct modulation of the haemodynamic response.

Neuronal oscillations are ubiquitous in the cortex and may contribute to cognition in a number of ways, for example by segregating information and organizing spike timing. Recent data show that delta, theta, and gamma oscillations are specifically engaged by the multi-timescale, quasi-rhythmic properties of speech and can track its dynamics. We argue that they play a foundational role in speech and language processing by ‘packaging’ incoming information into units that have a linguistic value, e.g. syllables. Such stimulus-brain alignment arguably results from auditory and motor tuning throughout the evolution of speech and language and constitutes a natural paradigm allowing auditory research to make a distinct contribution to the role of neural oscillatory activity in human cognition.

TITLE: The Mechanisms Underlying Neocortical Dynamics and Their Meaning for Perception: Integrating Correlative and Causal Methods

Searching for Meaningful Human States Using MEG, Psychophysics and Realistic Modeling

My laboratory studies how neocortical dynamics contribute to perception. To ground our studies in human relevance, we conduct human neurophysiological recording (MEG) combined with psychophysical testing. These studies examine how emergence of dynamics (e.g., rhythmic oscillatory states such as alpha) impacts perceptual information processing. To understand the neural underpinnings of these dynamics, we apply biophysically realistic neural modeling that make direct predictions as to the neocortical cell types and patterns of activity underlying oscillations and evoked sensory responses. These human studies are led by our long-term collaborator, Dr. Stephanie Jones.

We then systematically test the mechanisms and meaning of these dynamics in mouse models, which allow us to leverage the revolution in genetic engineering in a tractable mammalian model. This preparation allows us to target optogenetic control—the use of light to drive activity—to specific cell types. As one example, we recently demonstrated that selective activation of the thalamic reticular nucleus can generate realistic sleep spindles and shift the mode of thalamic transmission from tonic to burst-firing mode. Similarly, we previously employed this form of selective control to generate realistic gamma oscillations in neocortex by driving fast-spiking interneurons.

Causally Testing the Meaning of Neural Oscillations for Information Processing

In ongoing studies, we are now testing the hypothesis that introduction of gamma enhances sensory processing in mice performing a sensory detection task. We find that optogenetically-induced gamma can enhance detection probability of sinusoidal and naturalistic stimuli. This finding provides direct, causal evidence in support of the long-debated hypothesis that gamma rhythm benefits processes such as attention.

In recent years, dynamic causal modelling has become established in the analysis of invasive and non-invasive electromagnetic signals. In this talk, I will briefly review the basic idea behind dynamic causal modelling – namely to equip a standard electromagnetic forward model, used in source reconstruction, with a neural mass or field model that embodies interactions within and between sources. A key point here is that the resulting forward or generative models can predict a large variety of data features – such as event or induced responses, or indeed their complex cross spectral density – using the same underlying neuronal model.

Dynamic causal modelling allows people to compare alternative models or hypotheses based on different networks, using Bayesian model selection. Furthermore, Bayesian model inversion provides posterior estimates of model parameters that have a direct physiological interpretation – such as extrinsic (between-source) connection strengths or synaptic rate constants. This ability to test hypotheses and quantify neuronal parameters at the synaptic level holds great promise for non-invasive studies of health and disease. I hope to illustrate these points using examples from our collaborations, with a special focus on psychopharmacological studies and patient studies.

Dynamic causal modelling brings a new perspective to characterising event and induced responses – empirical response components, previously reified as objects of study in their own right (such as the mismatch negativity or P300) now become data features that have to be explained in terms of neuronal dynamics and changes in distributed connectivity. In other words, dynamic causal modelling emphasises the neurobiological mechanisms that underlie responses – over all channels and peristimulus time – without particular regard for the phenomenology of classical response components. My hope is to incite some discussion of this shift in perspective and its implications.